Studies of HIV transmission within populations (i.e., transmission networks) have demonstrated the critical importance of highly connected individuals (i.e., defined by highly related HIV strains indicative of putative transmissions) in sustaining the rate of HIV epidemic spread. The high mutation rate associated with HIV replication provides a nearly unique HIV genetic sequence (i.e., a fingerprint equivalent) in infected individuals resulting in an opportunity to study patterns of transmission network structure. While molecular epidemiologic data are used to track changes in epidemic course and geography, they are rarely (if ever) applied in real time to refocus prevention and treatment interventions to discrete populations or clusters within a population. Because partial HIV-1 pol sequences are generated for routine drug resistance testing, the data necessary to perform molecular analyses are readily available. Paired with appropriate epidemiologic data, network analyses can be used to identify emerging epidemics within groups of individuals related by similar patterns of illicit substance use, drug resistance, sexually transmitted infection, venues f exposure and stage of HIV infection that are appropriate targets for treatment and prevention interventions. Despite the significant public health advantage to using HIV genetic data to study and potentially intervene on real-time network spread of disease, these studies have been limited by fears of loss of privacy related to hypothetical disclosure of potential transmission between two or more individuals. Such disclosure of putative transmission between two or more individuals represents a concern for both consumers and healthcare providers. Unfortunately, no metrics exist to quantify this risk, and no guidelines exist for the use of non-host genetics to target interventions for infectious epidemics. The lack of guidelines in this sensitive area limits progress and research in the molecular epidemiology of HIV. There is an urgent need for guidelines based on both perspectives of the diverse stakeholders and on better definition and quantification of the limits of privacy risk. Our overall goal is to characterize perceptions of privacy risk among stakeholders and reduce uncertainty regarding privacy risk in HIV network analysis. To address the ethical challenges highlighted by the conflict between the public health potential to limit HIV transmission and the need to maximally protect personal healthcare privacy, we propose two Specific Aims: 1) Assess perceptions regarding HIV transmission network analyses. and 2) demonstrate strong quantifiable privacy associated with transmission network analysis. This proposal will be the first to begin framing the risk-benefit ratio for the ue of HIV genetic data used for molecular epidemiologic analyses to target interventions based on HIV transmission networks. The outcomes of these analyses may serve as a foundation for future discussions of genetic investigations of outbreaks and spread of other human pathogens.